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Impact of Land use Change and Urbanization on Urban Heat Island in Lucknow City, Central India. A Remote Sensing Based Estimate
Abstract
In this paper, the negative impact of urbanization over a time and its effect on increasing trend of temperature and degradation of urban ecology was assessed using the Landsat thermal data and field survey of Lucknow city, India. Land surface temperature (LST) estimation has been carried out using Mono-window algorithm, temporal land use change map, assessment of vegetation cover through Normalized Difference Vegetation Index (NDVI), and ecological evaluation of the city was carried out using the Urban Thermal Field Variance Index (UTFVI). Results indicated that the spatial distribution of the land surface temperature was affected by the land use-land cover change and anthropogenic causes. The mean land surface temperature difference between the years 2002 and 2014 was found is 0.75 °C. The observed results showed that the central portion of the city exhibited the highest surface temperature compared to the surrounding open area, the areas having dense built-up displayed higher temperatures and the areas covered by vegetation and water bodies exhibited lower temperatures. Strong correlation is observed between Land surface temperatures with Normalized Difference Vegetation Index (NDVI) and UTFVI. The observed LST of the area also validated trough the Google Earth Images. Ecological evaluation of the area also showed that the city has worst ecological index in the highly urbanized area in the central portion of the city. The present study provides very scientific information on impact of urbanization and anthropogenic activities which cause major changes on eco-environment of the city.
... Liu et al., 2020;Zhou et al., 2014). Similarly, built-up areas exhibited the highest LST in India, while vegetated and water-covered regions had lower temperatures, reinforcing the negative correlation between vegetation and LST (Singh et al., 2017). ...
... In major Chinese cities, reduced vegetation increased SUHII, particularly during summer (Yao et al., 2017). Similarly, urban expansion and land cover changes in India contributed to rising temperatures, with the most urbanized areas showing the worst ecological conditions (Singh et al., 2017). These impacts disrupt local ecosystems and contribute to broader global challenges, including climate change and declining air quality (Mushore et al., 2022;Portela et al., 2020). ...
... In Saudi Arabia's Jubail Industrial City, rapid industrialization has led to extensive land cover changes, where increased built-up areas and reduced vegetation have significantly contributed to extreme surface temperatures reaching 50-55 • C (Sheik Mujabar, 2019). Similarly, urban expansion in India's urban-industrial zones has led to rising LST, with built-up areas exhibiting the highest temperatures while vegetated areas remain cooler, reinforcing the negative correlation between vegetation cover and surface temperature (Singh et al., 2017). ...
Industrialization has become a major environmental challenge. It intensifies urban heat island effects, degrades vegetation, and increases carbon emissions, particularly in rapidly developing regions. However, quantifying and mitigating these impacts remains a significant challenge, especially in cloud-prone areas where remote sensing data is often compromised. This study employs an advanced machine learning-based approach to assess the environmental impacts of industrial activities in Cilegon, Indonesia. Multi-source remote sensing datasets from 2014 to 2022 were analyzed. Integrating Landsat-8 for Land Surface Temperature (LST) and vegetation health (NDVI), GPM for precipitation, and ODIAC for carbon emissions. A hybrid filter approach enhanced data quality by reducing cloud-induced noise. An XGBoost model was developed to reconstruct LST, enabling a spatiotemporal assessment of industrial heat island (IHI) dynamics. The results reveal a staggering increase in carbon emissions, with Industrial Area 2 emitting 107 times more carbon than Industrial Area 1 due to coal-fired power plants. The industrial heat island effect extends to 1500 m, while carbon emissions significantly influence areas within 1000 m, exacerbating environmental stress. NDVI analysis indicates an 81.36 % decline in healthy vegetation in Industrial Area 2 between 2014 and 2019, emphasizing the severe ecological impact of industrialization. Seasonal trends show that La Ni˜na-induced precipitation partially aids vegetation recovery, but its effects cannot counterbalance industrial degradation. Strong negative correlations were observed between NDVI and LST (-0.928) and NDVI and carbon emissions (-0.739), reinforcing the crucial role of vegetation in mitigating environmental damage. This study presents a data-driven framework to assess industrial environmental impacts using machine learning and enhanced satellite processing. The findings emphasize the urgent need for stricter emissions control, urban greening, and sustainable industrial planning to mitigate environmental degradation. Given the global rise in industrial emissions and urban heat stress, similar mitigation strategies are essential to enhance climate resilience and ecosystem sustainability.
... This study provides valuable insights into the dynamics of urban microclimates and the impact of land surface characteristics on temperature changes in Lucknow and Delhi. The land surface temperature trends over the years (Table 4) [62] used remote sensing techniques to assess UHI patterns in Lucknow. They found that high-density built-up regions such as Aminabad, Aliganj, and Charbagh exhibited the highest LST values, ranging from 33-42°C, a trend reflected in our findings. ...
This study compares the Urban Heat Island (UHI) phenomenon in two Indian metropolitan cities: Lucknow and Delhi. This study helps to understand the comparative impact of urbanisation on the UHI effect in mid-sized and large urban cities using multi-temporal satellite data and index-based Analysis. MODIS satellite data were used to examine the UHI, while Landsat 8 data helped extract the land surface features of both cities. Various indices, such as NDVI, MBI, MNDWI, and NDBaI, were utilised to study the land surface characteristics of the study area using GIS-based tools and methods. The study findings indicate that about 18.15% of Lucknow is classified as a High Potential Urban Heat Island (UHI) Zone, compared to approximately 17.17% in Delhi. Land surface temperatures (LST) in Lucknow rose from 38.11°C and 30.41°C in 2000 to 46.17°C and 39.15°C in 2023. Similarly, in Delhi, LST values increased from 38.35°C and 24.49°C in 2000 to 47.27°C and 32.93°C in 2023. These zones are typically found in locations with high built-up land density and unplanned development activities. The study identifies a negative correlation between the UHI and the presence of green and blue spaces, which can help reduce the intensity of the UHI. The research emphasises the importance of understanding and managing the UHI effect in highly urbanised areas, as this knowledge will assist policymakers and stakeholders in enhancing livability and sustainability within cities.
... There was a noticeable seasonal variation in the relationship between UHII and air pollutants. Prior research has demonstrated that urbanization increases air pollution and LST in a number of urban regions [104,105]. Ulpiani [106] presented a review of the research from the last 30 years on the relationship between LST and air pollution and recommended that, going forward, it would be crucial to examine these parameters jointly rather than individually. Lai and Cheng [107] found that, in the Taiwan Strait, China, surface urban heat island (SUHI) had a negative correlation with O3 and a positive correlation with key pollutants, such as PM, NO2, SO2, and CO. ...
Egypt must present a more thorough and accurate picture of the state of the air, as this can contribute to better environmental and public health results. Hence, the goal of the current study is to map and track the spatiotemporal air quality over Egypt's Qena Governorate using remote sensing data. The current investigation is considered a pioneering study and the first attempt to map the air quality index in the studied area. Mul-tisource remote sensing data sets from the Google Earth Engine (GEE) were used to achieve this. The first is Sentinel-5P's average annual satellite image data, which were gathered for four important pollutants: carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3) over a six year period from 2019 to 2024. The second is the MODIS aerosol optical density (AOD) product satellite image data from the GEE platform, which calculate the average annual particulate matter (PM2.5 and PM10). All mentioned pollutant images were used to calculate the air quality index (AQI) and aggregated air quality index (AAQI). Lastly, we used Landsat's average yearly land surface temperature (LST) retrieval (OLI/TIRS). The aggregated air quality index (AAQI) was computed, and the U.S. Environmental Protection Agency's (USEPA) air quality index (AQI) was created for each pollutant. According to the data, the AQI for CO, PM2.5, and PM10 in the research region ranged from hazardous to unhealthy; at the same time, the AQI for NO2 varied between harmful and unhealthy for sensitive groups, with values ranging from 135 to 165. The annual average of the AQI for SO2 throughout the studied period ranged from 29 to 339, with the categories ranging from good to hazardous. The constant AQI for ozone in the study area indicates that the ozone doses in Qena are surprisingly stable. Lastly, with a minimum value of 265 and a maximum of 489, the AAQI ranged from very unhealthy to dangerous in the current study. According to the data, the area being studied has poor air quality, which impacts the environment and public health. The results of this study have significant implications for environmental sustainability and human health and could be used in other areas. Citation: Mustafa, A.-r.A.; Shokr, M.S.; Alharbi, T.; Abdelsamie, E.A.; El-Sorogy, A.S.; Meroño de Larriva, J.E. Integration of Google Earth Engine and Aggregated Air Quality Index for Monitoring and Mapping the Spatio-Temporal of Air Quality to Improve Environmental Sustainability in Arid Regions. Sustainability 2025, 17, 3450.
... Maximizing and properly taking advantage of these characteristics can lower investment and maintenance costs, increase the aesthetic value of greenery, and improve botanical, hygienic, and functional value, of parks thereby extending the life of existing vegetation, but limiting the appearance of pollinators (Norton et al. 2019;Tanis et al. 2020). Greenery also has climatic significance, as it creates favorable microclimates, among other things, by regulating soil and air moisture through transpiration or the evaporation of water from aboveground parts of plants; this contributes to cooling the air and saturating it with humidity (Singh et al. 2017;Yu et al. 2020). In urban roundabouts, greenery can serve an anti-smog function, creating a beneficial ecosystem created for the environment and natural surroundings. ...
Climate neutrality requires urban infrastructure overhauls, particularly in rainwater management and green space expansion. Urban planning often conflicts with hydrological engineering, straining budgets and limiting green spaces. This review analyzed urban forestry, greenery, and ecohydrological policies in housing and buildings using bibliometric network analysis from Scopus and Web of Science. Key terms such as "urban climate" and "adaptive management" were linked, highlighting challenges in climate change implementation. Vegetation-based rainwater management projects improved resident safety and reduced sewage discharge through soil retention and evapotranspiration. Strategic greenery (green roofs, gardens, parks) enhances water capture and storage. Findings showed that integrating blue–green infrastructure mitigates conflicts between gray infrastructure and hydrological engineering, reducing construction pressures and preserving urban parks. New stormwater management technologies should align with urban ecosystems and housing priorities. Effective ecohydrological policies require careful local planning and appropriate tools to balance sustainability and urban development.
... The UHI effect can impact local weather and climate by altering wind patterns and precipitation rates [6]. Impervious surfaces in cities, including building roofs, parking lots, roads, transportation infrastructure, tall buildings, and areas without vegetation (e.g., sandy or barren regions), primarily contribute to the UHI effect due to air trapping and airflow reduction [7]. ...
Urban heat islands (UHI) disrupt the environmental balance in urban areas. The rate and impact of this formation are intensified by the increase in impervious surfaces, along with urban sprawl and land use changes. This increase reduces natural water absorption, causing heat retention and airflow restriction. Changes in land cover alter surface albedo, affecting energy interactions between the atmosphere and the surface, leading to local climate change. Remote detection methods are important tools for data gathering and assessing UHIs. This study uses a spatiotemporal analysis to examine the relationship between urban development and UHI change between 2005 and 2021, focusing on the Etimesgut district, located on the western development axis of Ankara, where land use changed from agricultural and public land to large-scale residential areas in the last three decades, in particular along with the development of mass housing by Housing Development Agency. Therefore, the district was under pressure for urban growth and mainly developed by transferring public property to private property. The analysis explains how urban sprawl increases land surface temperature (LST), contributing to the formation of UHIs. This study shows that in the Etimesgut district, where the built-up area has increased significantly between 2005 and 2021, from 9,040 ha to 12,934 ha, showing a 43.08% increase, there has been an increase in the LST by about four °C, rising from 43.33°C to 47.02°C in July. Satellite imagery-based findings indicate that the replacement of agricultural land by built-up areas accelerates the rise in temperatures in the region by weakening natural cooling mechanisms. This study intends to inform urban policy and policy development and offers an evidence-based approach using the Etimesgut district in Ankara as a case study. Urban development policies should cover climate-prone strategies and thermal governance to mitigate the UHI effects when barren or agricultural land is replaced with impervious surfaces of large-scale residential areas.
... UTFVI has been estimated using Eq. 8 (P. Singh et al. 2017). ...
The pattern and rate of urbanization in a city prevalently offer a comprehensive insight into the interactions between Earth’s terrestrial surface and anthropogenic activities. The study presented here comprehensively investigated the cooling effects of urban green spaces (UGSs) to address a critical research gap in Meerut, a city in India. A Spatial analysis approach was used to examine the relationship among the UGSs, heat mitigation, and temperature variables using the InVEST Urban Cooling Model (UCM) tool. This study explored the relationships between the Heat Mitigation Index (HMI) and three key variables—Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Air Temperature (AT)—using Landsat 9 data from April 2023. Most of the city is covered under fallow land with LST ranging from 27.18℃ to 45.39℃, and the Urban Thermal Field Variance Index (UTFVI) classifies it as excellent (47.06%). The InVEST model was utilized to determine the cooling capacity potential, which varies from 0.04 to 0.92. The Wet Bulb Globe Temperature Index (WBGT) ranging between 32.41 to 37.10 indicated significant heat risk related discomfort. It emphasises the importance of green infrastructure in city development plans to reduce heat and improve comfort. A comprehensive understanding of urban cooling services can contribute to informed decision-making for climate action and sustainable urban development.
In the context of increasing urbanization and climatic change, the global phenomenon of Urban Heat Island (UHI) has become a critical concern, with urban surface temperatures steadily increasing across cities worldwide. UHI has intensified over recent decades, consequently exerting a substantial impact on the local climate and affecting the quality of human life. UHI is a phenomenon in which urban regions experience higher temperatures compared to the adjacent outlying areas, as a result of urbanization, population pressure, industrialization, insufficient vegetation, and transportation systems. The present study is focused on Jhansi, aiming to comprehensively analyze the formation of UHIs in the region. By employing a multidimensional approach, the research investigates the dynamic changes in land use/land cover and f luctuations in urban surface temperature in Jhansi as well as correlation analysis is determined through remote sensing and meteorological data, and Geographic Information System (GIS) analysis. The findings of the study indicate that there is an overall increase in the intensity of UHI from 2001 to 2021 and this is because of the conversion of permeable surfaces like open spaces, water bodies, and vegetation towards impermeable surfaces like buildings, roads, and other infrastructure. Higher temperatures were observed in urban areas characterized by dense built-up structures and areas devoid of vegetation, but lower temperatures were seen in regions with vegetation and waterbodies. The findings also reveal that LST demonstrates a negative correlation with NDVI, signifying the cooling impact of green spaces, and a positive association with NDBI, underscoring the warming effect of built-up areas on the urban microclimate. These findings are crucial for urban planners and policymakers to devise effective mitigation and adaptation strategies while dealing with increasing urbanization and climate change. The study helps in developing an in-depth analysis of UHI formation, with implications extending beyond Jhansi to urban centers globally, which can become a foundation for evidence-based decision-making for more resilient and sustainable urban environments.
The rapid pace of urbanization has significantly altered land use/land cover (LULC) patterns, replacing natural landscapes with impervious surfaces, thereby intensifying the urban heat island (UHI) effect. This study evaluates the effectiveness of nature-based solutions (NBS) in mitigating UHI intensity in Qaem Shahr, Iran, by integrating remote sensing data and the InVEST-Urban Cooling Model (UCM). LULC maps for 2003, 2013, and 2023 were derived from Landsat imagery, while projections for 2033 were modeled under business-as-usual (BAU) and NBS scenarios using the CA-Markov model. The UCM was employed to analyze spatial changes in the Heat Mitigation Index (HMI), considering the biophysical attributes of urban green and blue spaces, such as shade, evapotranspiration, and albedo. Results revealed significant decreases in tree cover (15.2 km²) and water bodies (3.8 km²) from 2003 to 2013, alongside expansions in agricultural land (55.5 km²) and human settlements (26.3 km²). By 2023, only 67% of the region maintained a high cooling capacity (CC > 0.9), compared to 91% in 2003. Future projections indicate a 23% reduction in cooling capacity under BAU, even with NBS implementation. Tree cover demonstrated the highest cooling potential, reducing UHI intensity by 3.32°C on average, followed by grasslands at 3.21°C. The findings of this study emphasize the importance of implementing NBS strategies, such as the expansion of urban vegetation, the use of green roofs and walls, in mitigating the effects of urban heat islands and achieving sustainable urban development.
Background:
Tibet is especially vulnerable to climate change due to the relatively rapid rise of temperature over past decades. The effects on mortality and morbidity of extreme heat in Tibet have been examined in previous studies; no heat adaptation initiatives have yet been implemented. We estimated heat vulnerability of urban and rural populations in 73 Tibetan counties and identified potential areas for public health intervention and further research.
Methods:
According to data availability and vulnerability factors identified previously in Tibet and elsewhere, we selected 10 variables related to advanced age, low income, illiteracy, physical and mental disability, small living spaces and living alone. We separately created and mapped county-level cumulative heat vulnerability indices for urban and rural residents by summing up factor scores produced by a principal components analysis (PCA).
Results:
For both study populations, PCA yielded four factors with similar structure. The components for rural and urban residents explained 76.5 % and 77.7 % respectively of the variability in the original vulnerability variables. We found spatial variability of heat vulnerability across counties, with generally higher vulnerability in high-altitude counties. Although we observed similar median values and ranges of the cumulative heat vulnerability index values among urban and rural residents overall, the pattern varied strongly from one county to another.
Conclusions:
We have developed a measure of population vulnerability to high temperatures in Tibet. These are preliminary findings, but they may assist targeted adaptation plans in response to future rapid warming in Tibet.
In the present research work an integrated use of Landsat thermal data sets of year 2000 and 2013, field data and meteorological observation were used to assess the temporal changes in rising trends of urban heat island (UHI) in Noida city, India. The temperature estimation was performed on the basis of grid level analysis and compared with the land cover pattern for validation of temperature with reference to urban land use/land cover. During 2000, the total built up area was 28.17 km2 which it further increased to 88.35 km2 during 2013. Over the period of thirteen years from 2000 to 2013 it was observed that the built up area has increased by 26.94% of the total area (203 km2). In order to study the relationship between UHI and land cover, statistical analysis was performed between temperature and Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Albedo and Emissivity. The correlation between NDVI, Emissivity and temperature was negative but NDBI, Albedo and temperature showed a positive correlation. Results showed that the change in temperature was mainly due to increase in impervious areas. The results of this study will be useful to the urban planners and environmentalists in formulating local policies.
In the last few decades extreme heat events have led to substantial excess mortality, most dramatically in Central Europe in 2003, in Russia in 2010, and even in typically cool locations such as Vancouver, Canada, in 2009. Heat-related morbidity and mortality is expected to increase over the coming centuries as the result of climate-driven global increases in the severity and frequency of extreme heat events. Spatial information on heat exposure and population vulnerability may be combined to map the areas of highest risk and focus mitigation efforts there. However, a mismatch in spatial resolution between heat exposure and vulnerability data can cause spatial scale issues such as the Modifiable Areal Unit Problem (MAUP). We used a raster-based model to integrate heat exposure and vulnerability data in a multi-criteria decision analysis, and compared it to the traditional vector-based model. We then used the Getis-Ord Gi index to generate spatially smoothed heat risk hotspot maps from fine to coarse spatial scales. The raster-based model allowed production of maps at spatial resolution, more description of local-scale heat risk variability, and identification of heat-risk areas not identified with the vector-based approach. Spatial smoothing with the Getis-Ord Gi index produced heat risk hotspots from local to regional spatial scale. The approach is a framework for reducing spatial scale issues in future heat risk mapping, and for identifying heat risk hotspots at spatial scales ranging from the block-level to the municipality level.
p> OBJECTIVES: Extreme heat is known to increase heat-related health outcomes (HRHO). Incidence and predictors of HRHO were examined among older adults living in Quebec (Canada).
METHOD: This prospective five-year study used data from the first follow-up of community-dwelling older adults from the NuAge cohort (2005–2006), located in three health regions of Southern Quebec. Medical, social and environmental factors, identified in Health Canada guidelines (2011), were used to develop the Older Adult Health Vulnerability Index (OAHVI). HRHO, obtained from a medico-administrative database, were defined as events occurring on a hot day (maximal temperature ≥30°C) between 2006 and 2010. Two outcomes were examined: heat-related 1) emergency department presentations (EDPs) and 2) health events (i.e., EDP, hospitalizations or deaths). Multivariate logistic regressions were performed to assess the associations between risk and protective factors, including OAHVI, and both outcomes.
RESULTS: EDP and hospitalizations were, respectively, 2.6 (95% CI: 2.0–3.5) and 1.7 (95% CI: 1.1–2.6) times more frequent on hot days compared to normal summer days. Low household income and disability increased risk of heat-related EDP (AOR = 3.20; 95% CI: 1.16–8.81 and AOR = 2.66; 95% CI: 1.15–6.14 respectively) and health events (AOR = 2.84; 95% CI: 1.06–7.64 and AOR = 2.51; 95% CI: 1.13–5.61 respectively). High social participation was a protective factor of heat-related EDP (AOR = 0.05; 95% CI: 0.01–0.20) and health events (AOR = 0.04; 95% CI: 0.01–0.18). Older adults presenting ≥6 OAHVI factors out of 9 were 7–8 times more at risk of heat-related EDP (OR = 7.40; 95% CI: 1.51–36.19) and health events (OR = 7.77; 95% CI: 1.63–37.20) compared to participants having 0–1 factor.
CONCLUSION: Social participation, reduced autonomy and low income were predictors of HRHO. The OAHVI, also a strong predictor, should help clinicians identify high-risk elderly patients.</p
Key Messages
Spatial patterns of heat vulnerability exist as a result of variations in the distribution of socio‐economic, environmental, and infrastructural factors.
105 socio‐economically deprived dissemination areas were found to be at higher risk for heat events; the effect of environmental and infrastructural factors may elevate this risk.
Results may inform both short‐term emergency management efforts and longer‐term urban planning interventions to reduce health effects of extreme heat events.
Extensive laboratory tests and field observations show that soft clay exhibits significant rate-dependent behavior. Based on published data from the view of the rate dependencies of preconsolidation pressure and undrained shear strength. The rate- dependencies of soft clays under both 1D and 3D conditions are first studied. The applicability of five rate-dependency equations (two equations in exponential form and three equations in logarithmic form) in correlating the preconsolidation pressure and the undrained shear strength with strain-rate is then discussed. Rate parameters under both 1D and 3D conditions for various clays are estimated by using five rate-dependency equations and correlated with Atterberg limits. Furthermore, the rate-dependencies of soft clays under complex stress conditions including shear vane tests and pressuremeter tests are also analyzed. Then, the uniqueness of rate-dependency between 1D and 3D, between triaxial compression and extension, between different OCR (over consolidation ration) conditions is discussed. Finally, the influence of applied strain-rate on the stress-dilatancy of Hong Kong marine clay for triaxial compression and tension tests under different OCR conditions is investigated with some typical stress-dilatancy equations. The results show that the existing typical stress-dilatancy relationships need further enhancement (e. g., amount, anisotropy) for better describing the stress-dilatancy of soft clays.
A decade after the implementation of prevention plans designed to minimise the impact of high temperatures on health, some countries have decided to update these plans in order to improve the weakness detected in these ten years of operation.
In the case of Spain, this update has fundamentally consisted of changing the so-called “threshold” or “trigger”
temperatures used to activate the plan, by switching from temperature values based on climatological criteria
to others obtained by epidemiological studies conducted on a provincial scale.
This study reports the results of these “trigger” temperatures for each of Spain's 52 provincial capitals, as well as the impact of heat on mortality by reference to the relative risks (RRs) and attributable risks (ARs) calculated for natural as well as circulatory and respiratory causes.
The results obtained for threshold temperatures and RRs show a more uniform behaviour pattern than those obtained
using temperature values based on climatological criteria; plus a clear decrease in RRs of heat-associated
mortality due to the three causes considered, at both a provincial and regional level as well as for Spain as a
whole.
The updating of prevention plans is regarded as crucial for optimising the operation of these plans in terms of
reducing the effect of high temperatures on population health.